COVID 19 pandemic is causing a global health epidemic. The biggest and most powerful tool to avoid COVID 19 is wearing a face mask, whenever we go outside or we are at public places. Due to this COVID 19 many governments in the world are forced to implement lockdown to deter virus transmission. According to so many survey reports, we can clearly observe that by wearing a mask at public places will reduce the risk of transmission of COVID 19 significantly. In this project, an IoT-enabled android application based smart gate or barricade that uses a deep learning model for face mask detection is used. The android application consists of other features such as recording details of visitors, verifying identification, recording vaccinations certificate. With the model proposed in this project we can use it in any shopping mall, hotel, apartment entrance, etc. Evaluation of the proposed framework is done by the Face Mask Detection algorithm using the TensorFlow software library. Besides, the ability to record and store details and verify id’s provides with security features that are foolproof. With the proposed system we can detect whether a person is wearing a mask or not by enabling the Internet of thing technology (IOT). In response to the ongoing COVID-19 pandemic, we present a robust deep learning pipeline that is capable of identifying correct and incorrect mask-wearing from real-time video streams. To accomplish this goal, we build a face mask detection model using transfer learning and embed the model with Arduino. CNN for detecting masks in the human face is constructed using sample datasets and MobileNetV2 which acts as an object detector in our case the object is mask. The technologies we use are OpenCV, TensorFlow, Neural Networks, IoT, Android Studio etc. So, with this face mask detection, as a part to stop the spread of the virus, we ensure that with this smart door we can prevent the virus from spreading and can regain our happy life.



Software And Hardware